| Título |
Road Perspective Depth Reconstruction from Single Images Using Reduce-Refirme- Upsamble CNNs* |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
16th Mexican International Conference on Artificial Intelligence, MICAI 2017 |
| Resumen |
Depth reconstruction from single images has been a challenging task due to the complexity and the quantity of depth cues that images have. Convolutional Neural Networks (CNN) have been successfully used to reconstruct depth of general object scenes; however, these works have not been tailored for the particular problem of road perspective depth reconstruction. As we aim to build a computational efficient model, we focus on single-stage CNNs. In this paper we propose two different models for solving this task. A particularity is that our models perform refinement in the same single-stage training; thus, we call them Reduce-Refine-Upsample (RRU) models because of the order of the CNN operations. We compare our models with the current state of the art in depth reconstruction, obtaining improvements in both global and local views for images of road perspectives. © Springer Nature Switzerland AG 2018. |
| Observaciones |
DOI 10.1007/978-3-030-02837-4_3, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), V. 10632 |
| Lugar |
Ensenada |
| País |
Mexico |
| No. de páginas |
30-40 |
| Vol. / Cap. |
10632 LNAI |
| Inicio |
2017-10-23 |
| Fin |
2016-10-28 |
| ISBN/ISSN |
9783030028367 |
